{"id":"W2162973902","doi":"10.1109/tcomm.2010.03.080151","title":"Efficient power allocation schemes for nonconvex sum-rate maximization on gaussian cognitive MAC","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Korea Advanced Institute of Science and Technology","keywords":"Cognitive radio; Mathematical optimization; Quadratic growth; Maximization; Relaxation (psychology); Gaussian; Quadratic equation; Interference (communication); Quadratic programming; Computer science; Quadratically constrained quadratic program; Channel (broadcasting); Simple (philosophy); Mathematics; Wireless; Algorithm; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002431638,0.001261028,0.001360714,0.0005236172,0.0005257011,0.001123433,0.001782337,0.0007936353,0.001511882],"category_scores_gemma":[0.004470064,0.0004671477,0.0005342726,0.001264914,0.001169701,0.001304471,0.001873363,0.00107512,0.0003379103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009647999,"about_ca_system_score_gemma":0.001165196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00204337,"about_ca_topic_score_gemma":0.002598904,"domain_scores_codex":[0.9984567,0.0006881588,0.00005728559,0.0001741663,0.0003964114,0.0002273655],"domain_scores_gemma":[0.9986762,0.0008479433,0.0001154102,0.0001249041,0.0001896814,0.00004593373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001498655,0.00008871855,0.0001205376,0.0001257561,0.00004641678,0.0001022187,0.0001379,0.8725719,0.00310089,0.06437513,0.0022807,0.05689994],"study_design_scores_gemma":[0.00001544028,0.00002715403,0.00002293321,0.000003809682,0.000005693819,0.0000149686,0.00001122753,0.9883371,0.0003243665,0.01093585,0.0002967218,0.000004666603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01292546,0.0002804422,0.9831108,0.0001325131,0.00003720423,0.00004454139,0.00002612716,0.00009890879,0.003344027],"genre_scores_gemma":[0.8309905,0.0005865314,0.164464,0.0002416518,0.0001213323,0.0001956051,0.00006931857,0.00008014208,0.003250839],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002431638,"threshold_uncertainty_score":0.01285982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02165640237990506,"score_gpt":0.2764396514206182,"score_spread":0.2547832490407131,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}